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Merge pull request #3338 from airqo-platform/satellite-data
setup job to retrieve satelite data
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| from datetime import datetime | ||
| from typing import List, Dict, Any | ||
|
|
||
| import ee | ||
| import numpy as np | ||
| import pandas as pd | ||
| from google.oauth2 import service_account | ||
|
|
||
| from airqo_etl_utils.config import configuration | ||
|
|
||
|
|
||
| class SatelliteUtils: | ||
| @staticmethod | ||
| def initialize_earth_engine(): | ||
| ee.Initialize( | ||
| credentials=service_account.Credentials.from_service_account_file( | ||
| configuration.GOOGLE_APPLICATION_CREDENTIALS, | ||
| scopes=["https://www.googleapis.com/auth/earthengine"], | ||
| ), | ||
| project=configuration.GOOGLE_CLOUD_PROJECT_ID, | ||
| ) | ||
|
|
||
| @staticmethod | ||
| def extract_data_for_image(image: ee.Image, aoi: ee.Geometry.Point) -> ee.Feature: | ||
| return ee.Feature( | ||
| None, | ||
| image.reduceRegion( | ||
| reducer=ee.Reducer.mean(), | ||
| geometry=aoi, | ||
| scale=1113.2, # TODO: Review this, possibly a need for custom scales. | ||
| ).set("date", image.date().format("YYYY-MM-dd")), | ||
| ) | ||
|
|
||
| @staticmethod | ||
| def get_satellite_data( | ||
| aoi: ee.Geometry.Point, | ||
| collection: str, | ||
| fields: List[str], | ||
| start_date: datetime, | ||
| end_date: datetime, | ||
| ) -> ee.FeatureCollection: | ||
| return ( | ||
| ee.ImageCollection(collection) | ||
| .filterDate(start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d")) | ||
| .filterBounds(aoi) | ||
| .select(fields) | ||
| .map(lambda image: SatelliteUtils.extract_data_for_image(image, aoi)) | ||
| ) | ||
|
|
||
| @staticmethod | ||
| def process_time_series( | ||
| time_series: Dict[str, Any], fields: List[str] | ||
| ) -> Dict[str, Dict[str, List[float]]]: | ||
| daily_data = {} | ||
| for feature in time_series["features"]: | ||
| date = feature["properties"]["date"] | ||
| if date not in daily_data: | ||
| daily_data[date] = {field: [] for field in fields} | ||
| for field in fields: | ||
| if field in feature["properties"]: | ||
| daily_data[date][field].append(feature["properties"][field]) | ||
| return daily_data | ||
|
|
||
| @staticmethod | ||
| def calculate_daily_means( | ||
| daily_data: Dict[str, Dict[str, List[float]]], fields: List[str], city: str | ||
| ) -> List[Dict[str, Any]]: | ||
| results = [] | ||
| for date, data in daily_data.items(): | ||
| result = { | ||
| "timestamp": datetime.strptime(date, "%Y-%m-%d").replace( | ||
| hour=0, minute=0, second=0, microsecond=0 | ||
| ), | ||
| "city": city, | ||
| } | ||
| for field in fields: | ||
| if data[field]: | ||
| result[field] = sum(filter(None, data[field])) / len(data[field]) | ||
| else: | ||
| result[field] = None | ||
| results.append(result) | ||
| return results | ||
|
|
||
| @staticmethod | ||
| def extract_data_for_location( | ||
| location: Dict[str, Any], | ||
| collections: Dict[str, List[str]], | ||
| start_date: datetime, | ||
| end_date: datetime, | ||
| ) -> List[Dict[str, Any]]: | ||
| aoi = ee.Geometry.Point(location["coords"]) | ||
| all_data = [] | ||
|
|
||
| for collection, fields in collections.items(): | ||
| prefixed_fields = [f"{collection}_{field}" for field in fields] | ||
| satellite_data = SatelliteUtils.get_satellite_data( | ||
| aoi, collection, fields, start_date, end_date | ||
| ) | ||
| time_series = satellite_data.getInfo() | ||
| daily_data = SatelliteUtils.process_time_series(time_series, fields) | ||
| prefixed_daily_data = { | ||
| date: { | ||
| f"{collection}_{field}": values for field, values in data.items() | ||
| } | ||
| for date, data in daily_data.items() | ||
| } | ||
| all_data.extend( | ||
| SatelliteUtils.calculate_daily_means( | ||
| prefixed_daily_data, prefixed_fields, location["city"] | ||
| ) | ||
| ) | ||
|
|
||
| return all_data | ||
|
|
||
| @staticmethod | ||
| def extract_satellite_data( | ||
| locations: List[Dict[str, Any]], | ||
| start_date: datetime, | ||
| end_date: datetime, | ||
| satellite_collections: Dict[str, List[str]], | ||
| ) -> pd.DataFrame: | ||
| SatelliteUtils.initialize_earth_engine() | ||
| all_data = [] | ||
| for location in locations: | ||
| all_data.extend( | ||
| SatelliteUtils.extract_data_for_location( | ||
| location, satellite_collections, start_date, end_date | ||
| ) | ||
| ) | ||
| all_data = pd.DataFrame(all_data) | ||
|
|
||
| df_fixed = all_data.groupby(["timestamp", "city"]).agg( | ||
| lambda x: x.dropna().iloc[0] if len(x.dropna()) > 0 else np.nan | ||
| ) | ||
|
|
||
| df_fixed.columns = df_fixed.columns.str.lower() | ||
| df_fixed.columns = [ | ||
| c.replace("/", "_").replace(" ", "_").lower() for c in df_fixed.columns | ||
| ] | ||
|
|
||
| return df_fixed |
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